人工智能大会2018讲师

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Huma Abidi is the engineering director of the Artificial Intelligence Product Group at Intel, where she is responsible for deep learning framework software optimization for Intel Xeon processors. Huma joined Intel as software engineer and has since worked in a variety of engineering, validation, and management roles in the area of compilers, binary translation, and machine learning and deep learning. She received the Intel Achievement Award for her work in the Software and Services Group and was twice recognized with the Intel Software Quality award. She is passionate about women’s education and serves on the board of directors at ROSHNI, a philanthropic organization that educates and supports underprivileged girls in India. Huma holds a BS in pre-med and chemistry and an MS in computer science from the University of Massachusetts.

Presentations

Intel has been optimizing deep learning frameworks (in collaboration with framework owners) for Intel Xeon processors based on its Skylake microarchitecture. Huma Abidi details these collaborative optimization efforts, particularly for TensorFlow and MXNet, explains how users can leverage these optimizations, and shares specific tuning tips to get the best performance on Skylake platforms.

Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science’s AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

Presentations

Precision medicine promises to revolutionize healthcare by delivering better health outcomes at lower cost by eliminating trial-and-error medicine, and Intel is working to make this a reality. Arjun Bansal shares emerging algorithms and models used to analyze healthcare data, including electronic health records, medical images, and pharmaceutical and genomics datasets.

Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines.

Yishay Carmiel is the founder of IntelligentWire, a company that develops and implements industry-leading deep learning and AI technologies for automatic speech recognition (ASR), natural language processing (NLP), and advanced voice data extraction, and is the head of Spoken Labs, the strategic artificial intelligence and machine learning research arm of Spoken Communications. Yishay and his teams are currently working on bleeding-edge innovations that make the real-time customer experience a reality—at scale. Yishay has nearly 20 years’ experience as an algorithm scientist and technology leader building large-scale machine learning algorithms and serving as a deep learning expert.

Presentations

Yishay Carmiel offers an overview of neural models in speech applications, covering the dominant techniques and the elements that have contributed to the rapid progress. Yishay also looks to the future, examining which problems still remain and how far we are from solving them.

Simon Chan is a senior director of product management for Salesforce Einstein, where he oversees platform development and delivers products that empower everyone to build smarter apps with Salesforce. Simon is a product innovator and serial entrepreneur with more than 14 years of global technology management experience in London, Hong Kong, Guangzhou, Beijing, and the Bay Area. Previously, Simon was the cofounder and CEO of PredictionIO, a leading open source machine learning server (acquired by Salesforce). Simon holds a BSE in computer science from the University of Michigan, Ann Arbor, and a PhD in machine learning from University College London.

Presentations

Building an end-to-end AI application in production is tremendously more complicated than simply doing algorithm modeling in a lab. Simon Chan explains how to cross the gap between AI research fantasy into real-world applications.

Jian Chang is a senior algorithm expert at the Alibaba Group, where he is working on cutting-edge applications of AI at the intersection of high-performance databases and the IoT, focusing on unleashing the value of spatiotemporal data. A data science expert and software system architect with expertise in machine learning and big data systems and deep domain knowledge on various vertical use cases (finance, telco, healthcare, etc.), Jian has led innovation projects and R&D activities to promote data science best practices within large organizations. He’s a frequent speaker at technology conferences, such as the O’Reilly Strata and AI Conferences, NVIDIA’s GPU Technology Conference, Hadoop Summit, DataWorks Summit, Amazon re:Invent, Global Big Data Conference, Global AI Conference, World IoT Expo, and Intel Partner Summit, and has published and presented research papers and posters at many top-tier conferences and journals, including ACM Computing Surveys, ACSAC, CEAS, EuroSec, FGCS, HiCoNS, HSCC, IEEE Systems Journal, MASHUPS, PST, SSS, TRUST, and WiVeC. He’s also served as a reviewer for many highly reputable international journals and conferences. Jian holds a PhD from the Department of Computer and Information Science (CIS) at University of Pennsylvania, under Insup Lee.

Presentations

Leon Chen is the product marketing manager for Unity Analytics and Machine Learning, where he is responsible for driving productization and the go-to-market strategy for Unity ML-Agents, a Unity toolkit that allows developers and researchers to create and implement new AI algorithms. Previously, Leon spent over nine years as a tech evangelist, solution manager, and business manager for companies including Oracle and Microsoft. Leon holds an MBA from the University of Texas at Austin.

Presentations

Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani and Leon Chen lead a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks.

Roger Chen is cofounder and CEO of Computable and program chair for the O’Reilly Artificial Intelligence Conference. Previously, he was a principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology. Before startups and venture capital, he was an engineer at Oracle, EMC, and Vicor. He also developed novel nanoscale and quantum optics technology as a PhD researcher at UC Berkeley. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Presentations

Blockchain technologies offer new internet primitives for creating open and online data marketplaces. Roger Chen explores how data markets can be constructed and how they offer a shared resource on the internet for AI-based research, discovery, and development.

Dr. Jike Chong is an accomplished executive and professor with experience across industry and academia.

Jike currently heads Data Science, Hiring Marketplace at LinkedIn. He was most recently the chief data scientist at Acorns, the leading micro-investment app in US with over four million verified investors, which uses behavioral economics to help the up-and-coming save and invest for a better financial future. Previously, Jike was the chief data scientist at Yirendai, an online P2P lending platform with more than $7B loans originated and the first of its kind from China to go public on NYSE; established and headed the data science division at Simply Hired, a leading job search engine in Silicon Valley; advised the Obama administration on using AI to reducing unemployment; and led quantitative risk analytics at Silver Lake Kraftwerk, where he was responsible for applying big data techniques to risk analysis of venture investment.

Jike is also an adjunct professor and PhD advisor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, where he established the CUDA Research Center and CUDATeaching Center, which focus on the application of GPUs for machine learning. Recently, he also developed and taught a new graduate level course on machine learning for Internet finance at Tsinghua University in Beijing, China, where he is serving as an adjunct professor.

Jike holds MS and BS degrees in electrical and computer engineering from Carnegie Mellon University and a PhD from the University of California, Berkeley. He holds 11 patents (six granted, five pending).

Jason (Jinquan) Dai is a senior principal engineer and CTO of big data technologies at Intel, where he is responsible for leading the global engineering teams (located in both Silicon Valley and Shanghai) on the development of advanced big data analytics (including distributed machine and deep learning), as well as collaborations with leading research labs (e.g., UC Berkeley AMPLab and RISELab). Jason is an internationally recognized expert on big data, cloud, and distributed machine learning; he is the program cochair of the O’Reilly AI Conference in Beijing, a founding committer and PMC member of Apache Spark, and the creator of BigDL, a distributed deep learning framework on Apache Spark.

Presentations

Baining Guo is a distinguished scientist at Microsoft and the deputy managing director of Microsoft Research Asia, where he works on computer graphics, computer vision, and video analysis. Previously, he was a senior staff researcher with Intel Research in the Silicon Valley. Baining holds a BS degree from Beijing University and MS and PhD degrees from Cornell University. He is an IEEE fellow and ACM fellow and a member of the Canadian Academy of Engineering.

Presentations

Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him on Twitter.

Presentations

Yufeng Guo walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you'll gain insight into deep learning and how it can apply to complex problems in science and industry.

Kristian Hammond is a chief scientist at Narrative and a professor of computer science and journalism at Northwestern University. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

Presentations

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

Mark Hammond is cofounder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works and has been thinking about AI throughout his career. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.

Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications.

Hsiao-Wuen Hon is corporate vice president of Microsoft, chairman of Microsoft’s Asia-Pacific R&D Group, and managing director of Microsoft Research Asia, where he drives Microsoft’s strategy for research and development activities in the Asia-Pacific region, as well as collaborations with academia. Hsiao-Wuen has been with Microsoft since 1995. Previously, he founded and managed Microsoft’s Search Technology Center and led the development of Microsoft’s search products (Bing) in Asia-Pacific. Prior to joining Microsoft Research Asia, he was the founding member and architect of the Natural Interactive Services Division at Microsoft Corporation. An IEEE fellow and a distinguished scientist of Microsoft, Hsiao-Wuen is an internationally recognized expert in speech technology. He has published more than 100 technical papers in international journals and at conferences.

Presentations

Yonggang Hu is a distinguished engineer and chief architect of platform computing at IBM. He has been working on distributed computing, grid, cloud, and big data for the past 20 years. Previously, Yonggang was vice president and application architect at JPMorgan Chase focusing on computational analytics and application infrastructure. Yonggang holds an MS in computer science from Peking University and an MBA from Cornell University.

Arthur Juliani is a machine learning engineer at Unity Technologies. A researcher working at the intersection of cognitive neuroscience and deep learning, Arthur is currently working toward a PhD at the University of Oregon.

Presentations

Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani and Leon Chen lead a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks.

Sangkeun Jung is a researcher at SK Telecom, where he focuses on natural language interfaces. He is the main developer for the NUGU AI speaker. Sangkeun has 13 years of experience in AI research and engineering. He holds a PhD in computer science and engineering.

Presentations

Natural language understanding is a core technology for building natural interfaces such as AI speakers, chatbots, and smartphones. Sangkeun Jung offers an overview of a spoken dialog system and recently launched AI speaker, NUGU, and shares lessons learned building a commercially efficient and sustainable natural language understanding system.

Yan Kou is the director of product at Insight Data Science, which over her tenure instituted the first in the market professional education program on data science in healthcare in the US. Over the past two years, Yan has directed 80+ data science projects on topics including consumer genomics, electronic medical records, natural language processing, deep learning, medical images, and wearables. Yan’s team is an official partner of Y Combinator and has partnered with many leading healthcare organizations, including Massachusetts General Hospital, Optum, the Broad Institute, Flatiron Health, Biogen, and many more. Yan has a background in human genomics and five years experience in data science and machine learning. Her research on complex human diseases such as cancer and autism has resulted in more than 2,000 citations. Yan was nominated as one of Forbes’s 30 under 30 in 2013.

Presentations

Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others.

Danny Lange is the vice president of AI and machine learning at Unity, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business from the app to self-driving cars as the head of machine learning, provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public as the general manager of Amazon Machine Learning, led a product team focused on large-scale machine learning for big data as principal development manager at Microsoft, was CTO of General Magic, Inc., worked on General Motor’s OnStar Virtual Advisor—one of the largest deployments of an intelligent personal assistant until Siri—as the founder of his own company Vocomo Software, and was a computer scientist at IBM Research. He’s a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.

Presentations

Danny Lange demonstrates the role games can play in driving the development of reinforcement learning algorithms. Danny uses the Unity Engine with the ML-Agents toolkit as an example of how dynamic 3D game environments can be utilized for machine learning research.

Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life.

Dong Li is a technical partner and senior software architect at Kyligence. Dong is also an Apache Kylin committer and PMC member and the tech lead for KyBot. Previously, Dong was a senior software engineer in the Analytics Data Infrastructure Department at eBay and a software development engineer in the Cloud and Enterprise Department at Microsoft, where he was a core member of the dynamics APAC team, responsible for developing next-generation cloud-based ERP solutions. Dong holds both a bachelor’s and master’s degree from Shanghai Jiao Tong University.

Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an IEEE Fellow and an ACM Fellow.

Presentations

We have made rapid progress in apply machine learning to solve perception, prediction and planning problems. However, there are fundamental challenges ahead. We need to learn more robust and abstract representations, understand driving scenes, and make decisions in multi-agent settings.

Tianhui Michael Li is the founder and president of the Data Incubator, a data science training and placement firm. Michael bootstrapped the company and navigated it to a successful sale to the Pragmatic Institute. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. He’s a regular contributor to the Wall Street Journal, Tech Crunch, Wired, Fast Company, Harvard Business Review, MIT Sloan Management Review, Entrepreneur, Venture Beat, Tech Target, and O’Reilly. Michael was a postdoc at Cornell Tech, a PhD at Princeton, and a Marshall Scholar in Cambridge.

Shaoshan Liu is the cofounder and chairman of PerceptIn, a company working on developing a next-generation robotics platform. Previously, he worked on autonomous driving and deep learning infrastructure at Baidu USA. Shaoshan holds a PhD in computer engineering from the University of California, Irvine.

Yinyin Liu is the head of data science for AIPG at Intel, where she works with a team of data scientists on applying deep learning and Intel Nervana technologies to business applications across different industry domains and driving the development and design of the Intel Nervana platform. She and the Intel AI Products Group team have developed open source deep learning frameworks, such as neon and Intel Nervana Graph, bringing state-of-the-art models on image recognition, image localization, and natural language processing into the frameworks. Yinyin has research experience in computer vision, neuromorphic computing, and robotics.

Zhenxiao Luo is leading Interactive Query Engines team at Twitter, where he focuses on Druid, Presto, and Hive. Before joining Twitter, Zhenxiao was running Interactive Analytics team at Uber. Previously, he worked on big data related projects at Netflix, Facebook, Cloudera, and Vertica. Zhenxiao is PrestoDB committer. He holds a master’s degree from the University of Wisconsin-Madison and a bachelor’s degree from Fudan University.

Presentations

Sherry Moore is a software engineer on the Google Brain team. Her other projects at Google include Google Fiber and Google Ads Extractor. Previously, she spent 14 years as a systems and kernel engineer at Sun Microsystems.

Presentations

Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.

Alan Qi is the vice president and chief data scientist of Ant Financial, where he leads the AI department that is helping build machine learning and deep learning platforms (including search and recommendation engines) and works with various business units to develop analytical and predictive solutions. Previously, he was a tenured associate professor in CS and statistics at Purdue University. Alan is an associate editor of JMLR and served as area chair of ICML. He received the A. Richard Newton Breakthrough Research Award and NSF Career award in 2011. Alan holds a PhD from MIT.

Presentations

Nishant Sahay is a senior architect in the Open Source COE lab at Wipro, where he is responsible for research and solution development in the area of machine learning and deep learning. Nishant has extensive experience in data analysis, design, and visualization. He has written articles on technology in online forums and presented at multiple open source conferences, such as OSI Days, GIDS, and CNCF-Kubeconf.

Presentations

Deep learning with ConvNet in particular has emerged as a promising tool in medical research labs and diagnostic centers to help analyze images and scans, and systems are now surpassing human capability for manual inspection. Nishant Sahay explains how to apply deep learning to analyze high-end microscope images and X-ray scans to provide accurate diagnosis.

Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata and Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.

Presentations

The tensor processing unit (TPU) is a LSI designed by Google for neural network processing. The TPU features a large-scale systolic array matrix unit that achieves an outstanding performance-per-watt ratio. Kazunori Sato explains how a minimalistic design philosophy and a tight focus on neural network inference use cases enables the high-performance neural network accelerator chip.

Hassan Sawaf is director of applied science and artificial intelligence at AWS, where he is responsible for driving the science and technology behind products like Amazon Lex, Amazon Comprehend, Amazon Translate, Amazon Transcribe, and other machine learning services. He has worked in the automatic speech recognition, computer vision, natural language understanding, and machine translation fields for 20+ years. Previously, he cofounded AIXPLAIN AG, a company focusing on speech recognition and machine translation (acquired by AppTek); served as chief operations officer at AppTek (acquired by SAIC); worked as chief scientist for human language technology at SAIC, where he worked on multilingual spoken dialogue systems; and established eBay’s machine translation and cognitive computing team and later led the company’s artificial intelligence team behind various language technology and computer vision innovations.

Presentations

Hassan Sawaf discusses Amazon’s efforts to enable the enterprise with machine learning capability, in particular with newly released AWS services like Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Polly, Amazon Lex, Amazon SageMaker, and explains how some of these correlate with other Amazon products and services.

With today’s device and user interface technology and also the advent of advanced machine learning and deep learning models, input and output modalities are converging in many different dimensions. Hassan Sawaf offers a brief overview of research in human language technology and machine learning in merging information that is captured by the senses of machines.

Zhefu Shi is a researcher at the University of Missouri, where he works on mathematical modeling, artificial intelligence, machine learning, and cloud computing. Zhefu holds a PhD in computer science and a master’s degree in math and statistics.

Presentations

It is critical to analyze the business impact of worldwide events on the financial market. Zhefu Shi explains how to use AI to analyze the impact of financial news using a financial data pipeline. Zhefu outlines how to extract financial entity information and use it to analyze business impact. All of the components use AI to enhance functionality.

Hendra Suryanto is chief data scientist at Rich Data Corporation. Hendra has over 20 years’ experience in data science, big data, business intelligence, and data warehousing spanning across data architecture, data science and data engineering, managing and designing end-to-end data analytics solution within Agile continuous delivery DevOps framework. Previously, Hendra was a lead data scientist in KPMG’s Advisory practice, where he advised KMPG’s clients globally in data science and big data projects, and worked for a number of leading organizations in various domain verticals, such as telecommunications, banking, fraud, risk, marketing, and insurance, including Westpac Bank, Commonwealth Bank Australia, Veda, Bupa, HCF, and Vodafone. Hendra holds a PhD in artificial intelligence, which he followed with postdoctoral research in machine learning.

Presentations

Hendra Suryanto shares a case study from a Canadian financial lender that his company helped transition from manual to automated credit decisioning, using gradient boosting machine and deep learning to build the model. In addition to modeling techniques, Hendra highlights the role feature engineering plays in improving model performance.

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe. Earlier, he worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

To achieve high accuracy when reasoning about text, you generally need to understand specific languages, jargon, domain-specific documents, and writing styles. David Talby explains how to train custom word embeddings, named entity recognition, and question-answering models on the NLP library for Apache Spark.

Natural language processing is a key component in many data science systems that must understand or reason about text. David Talby offers an overview of the NLP library for Apache Spark, which natively extends Spark ML to provide open source, fully distributed, and optimized versions of state-of-the-art NLP algorithms, covering the library's design and sharing working code samples in PySpark.

Presentations

Graham Williams is director of data science at Microsoft, where he is responsible for the Asia-Pacific region, an adjunct professor with the University of Canberra and the Australian National University, and an international visiting professor with the Chinese Academy of Sciences. Graham has 30 years’ experience as a data scientist leading research and deployments in artificial intelligence, machine learning, data mining, and analytics. Previously, he was principal data scientist with the Australian Taxation Office and lead data scientist with the Australian Government’s Centre of Excellence in Analytics, where he assisted numerous government departments and Australian industry in creating and building data science capabilities. He has also worked on many projects focused on delivering solutions and applications driven by data using machine learning and artificial intelligence technologies. Graham has authored a number of books introducing data mining and machine learning using the R statistical software.

Presentations

Le Zhang and Graham Williams demonstrate how R-user data scientists and AI developers can use cloud services for convenient experimentation and production. Join Le and Graham for a walk-through of a proposed method that favors a TTD requirement for enterprise-grade AI and data applications.

Bichen Wu is a PhD candidate at UC Berkeley, where he focuses on deep learning, computer vision, and autonomous driving.

Presentations

Mingxi Wu is the vice president of engineering at TigerGraph, a Silicon Valley startup that is building a world-leading real-time graph data platform. Over the past 15 years, Mingxi has been focusing on database research and data management software building, serving within Microsoft’s SQL Server Group and Oracle’s Relational Database Optimizer Group. He has won research awards from the most prestigious publication venues in database and data mining (SIGMOD, KDD, and VLDB). He holds five US patents on big data and three pending international patents on graph management. He’s currently working on an easy-to-use and highly expressive graph query language. Mingxi holds a PhD from the University of Florida, where he specialized in both databases and data mining.

Tony Xing is a senior product manager in the AI, data, and infrastructure (AIDI) team within Microsoft’s AI and Research Organization. Previously, he was a senior product manager on the Skype data team within Microsoft’s Application and Service Group, where he worked on products for data ingestion, real-time data analytics, and the data quality platform.

Hui Xiong is chief scientist at Baidu. He is a co-editor-in-chief of the Encyclopedia of GIS and an associate editor of IEEE Transactions on Data and Knowledge Engineering (TKDE), IEEE Transactions on Big Data, ACM Transactions on Knowledge Discovery from Data, and ACM Transactions on Management Information Systems. He also served as a program cochair (2013) and general cochair (2015) for the IEEE International Conference on Data Mining (ICDM) and a program cochair (2018) of the Research Track for the ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). At the moment, he is on leave from Rutgers University. He was elected an ACM Distinguished Scientist in 2014 for his outstanding contributions to data mining and mobile computing. He holds a PhD in computer science from the University of Minnesota-Twin Cities.

Yu Xu is the founder and CEO of TigerGraph, the world’s first native parallel graph database. He is an expert in big data and parallel database systems and has over 26 patents in parallel data management and optimization. Previously, Yu worked on Twitter’s data infrastructure for massive data analytics and was Teradata’s Hadoop architect leading the company’s big data initiatives. Yu holds a PhD in computer science and engineering from the University of California, San Diego.

Presentations

Hua Yang is a director of eBay’s search team in Shanghai, where he is part of eBay’s global search product team. He and his team are researching and developing machine learning and artificial intelligence technologies to provide the most relevant search experiences for eBay customers. Hua has 20 years of software development experience, including over a decade in digital advertising and search engine development at both Microsoft and eBay. Hua studied software engineering and artificial intelligence and holds a PhD in computer science from Vanderbilt University.

Presentations

Season Yang is an analytics fellow in McKinsey & Company’s risk practice. Previously, Season was a data scientist in residence at the Data Incubator, where he also contributes to curriculum development and instruction, and worked at NASA’s Goddard Space Flight Center, where he studied climate change models with data analysis. Season holds a double bachelor’s degree in applied mathematics and scientific computation and economics from UC Davis and a master’s in applied mathematics from Columbia, specializing in numerical computation.

Dr. Yu is the CTO of AsiaInfo Data, which provides big data and AI solutions to all three telecom carriers in China. Its distributed big data platform processes over 7PB of data daily. Before joining AsiaInfo, Dr. Yu served as VP Engineering and Chief Architect for Mafengwo, the largest online travel community in China, with over 100 mobile and online users. Previously, he was VP Engineering and Chief Architect at OpenX, responsible for the company’s data strategy, mobile product line, and overall architecture, consisting of more than 6000 servers and 15PB of data distributed in 5 global data centers. Dr. Yu is also a serial entrepreneur, co-founded two startups, Portaura in social mobile big data and Martsoft in e-commerce search engine.

Early in his career, he spent a number of years in HP Systems Research Center, one of the top research labs in the world, where he worked closely with numerous Turing Award recipients in browser technology, search engine, multimedia, and distributed file systems. Dr. Yu holds PhD in Computer Science and Engineering from UNSW and BS in Computer Sciences and BA in Mathematics from UT Austin. He has published papers and gave keynote speeches in numerous major international conferences.

Presentations

Reza Bosagh Zadeh is founder and CEO at Matroid and an adjunct professor at Stanford University, where he teaches two PhD-level classes: Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms. His work focuses on machine learning, distributed computing, and discrete applied mathematics. His awards include a KDD best paper award and the Gene Golub Outstanding Thesis Award. Reza has served on the technical advisory boards of Microsoft and Databricks. He is the initial creator of the linear algebra package in Apache Spark. Through Apache Spark, Reza’s work has been incorporated into industrial and academic cluster computing environments. Reza holds a PhD in computational mathematics from Stanford, where he worked under the supervision of Gunnar Carlsson. As part of his research, Reza built the machine learning algorithms behind Twitter’s who-to-follow system, the first product to use machine learning at Twitter.

Presentations

Reza Zadeh offers an overview of Matroid’s Kubernetes deployment, which provides customized computer vision and stream monitoring to a large number of users, and demonstrates how to customize computer vision neural network models in the browser. Along the way, Reza explains how Matroid builds, trains, and visualizes TensorFlow models, which are provided at scale to monitor video streams.

Reza Zadeh details three challenges on the way to building cutting-edge ML products, with a focus on computer vision, offering examples, recommendations, and lessons learned.

Le Zhang is a data scientist with Microsoft Cloud and Artificial Intelligence, where he applies cutting-edge machine learning and artificial intelligence technology to accelerate digital transformation for enterprises and startups on cloud. He’s helped numerous corporations develop and build enterprise-grade scalable advanced data analytical systems with a broad spectrum of application scenarios like manufacturing, predictive maintenance, financial services, ecommerce, and human resource analytics. Le specializes in cloud computing, big data technologies, and artificial intelligence. He enjoys sharing knowledge and learning from people and is a frequent speaker at industrial and academic conferences and community meetups. He holds a PhD in computer engineering.

Presentations

Le Zhang and Graham Williams demonstrate how R-user data scientists and AI developers can use cloud services for convenient experimentation and production. Join Le and Graham for a walk-through of a proposed method that favors a TTD requirement for enterprise-grade AI and data applications.

Ruiwen Zhang is a senior research statistician at SAS, where she focuses on machine learning and data mining. She holds a PhD from the Department of Statistics and Operation Research at the University of North Carolina at Chapel Hill.

Presentations

Drawing on several real-world cases, Ruiwen Zhang demonstrates how to visualize the structure of a probabilistic model and provide better insights into the model's properties, which can be further used to design and motivate new models. She also explains how to reduce the computational complexity required to perform inference and learning in sophisticated models using graphical models.

Xiatian Zhang is chief data scientist at TalkingData, where he is responsible for mobile big data mining and machine learning algorithm research and implementation. Xiatian has long engaged in data mining and machine learning research and has dozens of research papers in publication and sufficient patents. Previously, he worked for IBM’s China Research Institute, the Tencent data platform, and Huawei’s Noah’s Ark Lab.

Yi Zhang is a tenured professor at the University of California, Santa Cruz, and cofounder and CTO of Rulai. Yi has 20 years of research experience in AI. She has received various awards, including an ACMSIGIR best paper award, the National Science Foundation Faculty Career Award, a Google research award, a Microsoft research award, and an IBM research fellowship. She has been a program cochair, area chair, and PC member for various top international conferences. Yi has served as a consultant or a technical adviser for several companies and startups, including Alibaba, Toyota, and HP. She holds a PhD in computer science from Carnegie Mellon University.

Presentations

Bowen Zhou is the vice president of artificial intelligence platform and research at JD.com. A technologist and business leader of human language technologies, machine learning, and artificial intelligence, Bowen is keen on creating and advance relevant theory and applications that matter in the real world. Previously, Bowen worked at IBM for almost 15 years, where as the chief scientist of Watson Group, he was responsible for leading and aligning the group’s science agenda with IBM’s technical strategy and IBM Research’s cognitive computing and artificial intelligence agenda. Bowen was an IBM distinguished engineer, an integrated member of the executive team of IBM Watson, and director of AI Foundations, the first horizontal core learning and AI research lab at IBM Research.